#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Subsampling layer definition."""
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import numpy as np
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import torch
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import torch.nn.functional as F
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from funasr.modules.embedding import PositionalEncoding
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import logging
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from funasr.modules.streaming_utils.utils import sequence_mask
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class TooShortUttError(Exception):
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"""Raised when the utt is too short for subsampling.
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Args:
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message (str): Message for error catch
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actual_size (int): the short size that cannot pass the subsampling
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limit (int): the limit size for subsampling
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"""
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def __init__(self, message, actual_size, limit):
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"""Construct a TooShortUttError for error handler."""
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super().__init__(message)
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self.actual_size = actual_size
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self.limit = limit
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def check_short_utt(ins, size):
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"""Check if the utterance is too short for subsampling."""
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if isinstance(ins, Conv2dSubsampling2) and size < 3:
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return True, 3
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if isinstance(ins, Conv2dSubsampling) and size < 7:
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return True, 7
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if isinstance(ins, Conv2dSubsampling6) and size < 11:
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return True, 11
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if isinstance(ins, Conv2dSubsampling8) and size < 15:
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return True, 15
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return False, -1
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class Conv2dSubsampling(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/4 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling object."""
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super(Conv2dSubsampling, self).__init__()
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, odim, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 3, 2),
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torch.nn.ReLU(),
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)
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self.out = torch.nn.Sequential(
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torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
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pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
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)
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 4.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 4.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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return x, x_mask[:, :, :-2:2][:, :, :-2:2]
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def __getitem__(self, key):
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"""Get item.
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When reset_parameters() is called, if use_scaled_pos_enc is used,
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return the positioning encoding.
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"""
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if key != -1:
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raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
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return self.out[key]
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class Conv2dSubsamplingPad(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/4 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling object."""
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super(Conv2dSubsamplingPad, self).__init__()
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, odim, 3, 2, padding=(0, 0)),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 3, 2, padding=(0, 0)),
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torch.nn.ReLU(),
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)
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self.out = torch.nn.Sequential(
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torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
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pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
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)
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self.pad_fn = torch.nn.ConstantPad1d((0, 4), 0.0)
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 4.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 4.
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"""
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x = x.transpose(1, 2)
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x = self.pad_fn(x)
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x = x.transpose(1, 2)
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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x_len = torch.sum(x_mask[:, 0, :], dim=-1)
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x_len = (x_len - 1) // 2 + 1
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x_len = (x_len - 1) // 2 + 1
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mask = sequence_mask(x_len, None, x_len.dtype, x[0].device)
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return x, mask[:, None, :]
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def __getitem__(self, key):
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"""Get item.
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When reset_parameters() is called, if use_scaled_pos_enc is used,
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return the positioning encoding.
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"""
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if key != -1:
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raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
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return self.out[key]
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class Conv2dSubsampling2(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/2 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling2 object."""
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super(Conv2dSubsampling2, self).__init__()
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, odim, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 3, 1),
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torch.nn.ReLU(),
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)
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self.out = torch.nn.Sequential(
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torch.nn.Linear(odim * (((idim - 1) // 2 - 2)), odim),
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pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
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)
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 2.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 2.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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return x, x_mask[:, :, :-2:2][:, :, :-2:1]
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def __getitem__(self, key):
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"""Get item.
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When reset_parameters() is called, if use_scaled_pos_enc is used,
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return the positioning encoding.
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"""
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if key != -1:
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raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
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return self.out[key]
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class Conv2dSubsampling6(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/6 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling6 object."""
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super(Conv2dSubsampling6, self).__init__()
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, odim, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 5, 3),
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torch.nn.ReLU(),
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)
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self.out = torch.nn.Sequential(
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torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim),
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pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
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)
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 6.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 6.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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return x, x_mask[:, :, :-2:2][:, :, :-4:3]
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class Conv2dSubsampling8(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/8 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling8 object."""
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super(Conv2dSubsampling8, self).__init__()
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, odim, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(odim, odim, 3, 2),
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torch.nn.ReLU(),
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)
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self.out = torch.nn.Sequential(
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torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim),
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pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
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)
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 8.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 8.
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"""
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x = x.unsqueeze(1) # (b, c, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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if x_mask is None:
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return x, None
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return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2]
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class Conv1dSubsampling(torch.nn.Module):
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"""Convolutional 1D subsampling (to 1/2 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (torch.nn.Module): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, kernel_size, stride, pad,
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tf2torch_tensor_name_prefix_torch: str = "stride_conv",
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tf2torch_tensor_name_prefix_tf: str = "seq2seq/proj_encoder/downsampling",
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):
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super(Conv1dSubsampling, self).__init__()
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self.conv = torch.nn.Conv1d(idim, odim, kernel_size, stride)
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self.pad_fn = torch.nn.ConstantPad1d(pad, 0.0)
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self.stride = stride
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self.odim = odim
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self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
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self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
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def output_size(self) -> int:
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return self.odim
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def forward(self, x, x_len):
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"""Subsample x.
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"""
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x = x.transpose(1, 2) # (b, d ,t)
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x = self.pad_fn(x)
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x = F.relu(self.conv(x))
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x = x.transpose(1, 2) # (b, t ,d)
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if x_len is None:
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return x, None
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x_len = (x_len - 1) // self.stride + 1
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return x, x_len
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def gen_tf2torch_map_dict(self):
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tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
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tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
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map_dict_local = {
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## predictor
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"{}.conv.weight".format(tensor_name_prefix_torch):
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{"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
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"squeeze": None,
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"transpose": (2, 1, 0),
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}, # (256,256,3),(3,256,256)
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"{}.conv.bias".format(tensor_name_prefix_torch):
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{"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
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"squeeze": None,
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"transpose": None,
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}, # (256,),(256,)
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}
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return map_dict_local
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def convert_tf2torch(self,
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var_dict_tf,
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var_dict_torch,
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):
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map_dict = self.gen_tf2torch_map_dict()
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var_dict_torch_update = dict()
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for name in sorted(var_dict_torch.keys(), reverse=False):
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names = name.split('.')
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if names[0] == self.tf2torch_tensor_name_prefix_torch:
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name_tf = map_dict[name]["name"]
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data_tf = var_dict_tf[name_tf]
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if map_dict[name]["squeeze"] is not None:
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data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
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if map_dict[name]["transpose"] is not None:
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data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
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data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
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var_dict_torch_update[name] = data_tf
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logging.info(
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"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
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var_dict_tf[name_tf].shape))
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return var_dict_torch_update
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